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@@ -23,7 +23,7 @@ A key limitation of large language models (LLMs) is their inability to capture l
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  Although RAG typically relies on embedding-based retrieval, the embedding models themselves are also based on language models and therefore struggle with queries involving less-frequent entities, often failing to retrieve the crucial knowledge needed to overcome this limitation.
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- **Knowledgeable Embedding** enhances the performance with such queries by injecting real-world entity knowledge into embeddings, making them more *knowledgeable*.
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  **The entity knowledge is pluggable and can be dynamically updated with ease.**
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  Although RAG typically relies on embedding-based retrieval, the embedding models themselves are also based on language models and therefore struggle with queries involving less-frequent entities, often failing to retrieve the crucial knowledge needed to overcome this limitation.
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+ **Knowledgeable Embedding** enhances performance on such queries by injecting real-world entity knowledge into embeddings, making them more *knowledgeable*.
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  **The entity knowledge is pluggable and can be dynamically updated with ease.**
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